Risk analysis of bridge maintenance accidents: A two-stage LEC method and Bayesian network approach

IF 4.3 Q2 TRANSPORTATION
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引用次数: 0

Abstract

Bridge maintenance is a long-term process that is prone to accidents. Identifying and reducing hidden dangers is crucial in decreasing the occurrence of such accidents. This study proposes a two-stage risk evaluation model based on the likelihood exposure consequence (LEC) method, which includes an occurrence stage and a development stage. The model utilizes hidden danger data accumulated over a long period to reflect the current maintenance stage's risk level. Additionally, a risk prediction model based on the Bayesian network is established to better identify hidden dangers that have a significant impact on construction risk levels (CRLs). The models are validated using 50 weeks of hidden danger data obtained from a real-world bridge maintenance project. The results show that certain hidden dangers have high risk levels when the CRL is high, and small changes in the risk level of certain hidden dangers can have a significant impact on the CRL. This study's models can aid in the development of more targeted HD prevention measures.
桥梁维修事故风险分析:两阶段LEC法和贝叶斯网络方法
桥梁维护是一个长期过程,容易发生事故。识别和减少隐患对于减少此类事故的发生至关重要。本研究提出了一种基于可能性暴露后果(LEC)方法的两阶段风险评估模型,包括发生阶段和发展阶段。该模型利用长期积累的隐患数据来反映当前维护阶段的风险水平。此外,还建立了一个基于贝叶斯网络的风险预测模型,以更好地识别对施工风险等级(CRL)有重大影响的隐患。利用从实际桥梁维护项目中获取的 50 周隐患数据对模型进行了验证。结果表明,当 CRL 较高时,某些隐患的风险水平较高,而某些隐患风险水平的微小变化会对 CRL 产生重大影响。这项研究的模型有助于制定更有针对性的高清预防措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
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